Working with Data Assignment 1
Kate Byrne
31 October 2018
Setup
Required R libraries are below
Task 1: Quarterly Economic Indicators
1.1 Dublin Employment Trends
Figure 1 below is the plot of quarterly Dublin Employment Trends for the period 2006 - 2016.
As the specific dates are hard to read off Figure 1, I’ve created a table below of the dates of the minimum and maximum employment values for each sector. Finance is at its maximum value for 3 separate quarters.
| Sector | Min Quarter | Min Employment Rate | Min Time Period | Max Quarter | Max Employment Rate | Max Time Period | Min Max Employment Rate Diff |
|---|---|---|---|---|---|---|---|
| Construction | 1’13 | -1.1 | 29 | 4’07 | 1.8 | 8 | 2.9 |
| Retail | 4’13 | -1.2 | 32 | 2’08 | 2.3 | 10 | 3.6 |
| Transport | 3’15 | -1.6 | 39 | 4’10 | 1.9 | 20 | 3.5 |
| Tourism | 1’11 | -1.4 | 21 | 3’16 | 2.0 | 43 | 3.4 |
| IT | 4’10 | -1.7 | 20 | 1’16 | 2.2 | 41 | 3.9 |
| Scientific | 3’11 | -2.7 | 23 | 2’08 | 1.5 | 10 | 4.2 |
| Finance | 2’06 | -2.1 | 2 | 1’08 | 1.7 | 9 | 3.8 |
| Finance | 2’06 | -2.1 | 2 | 2’09 | 1.7 | 14 | 3.8 |
| Finance | 2’06 | -2.1 | 2 | 3’09 | 1.7 | 15 | 3.8 |
From Figure 1 above, and the table of maximum and minimum values, it appears that construction, finance, retail and transport all largely follow the same pattern of peaking in the earlier time period (construction, finance and retail; late 2007/early 2008, transport; late 2010) and then decreasing.
There is a sharp decrease in both construction and retail, with a less sharp drop in both finance and transport. This would be influenced by the nature of these these industries. In both retail and construction, people are employed to directly provide a product for sale, if sales are down, staff are let go. As many staff in these industries can also be on insecure contracts, this can happen quite quickly. While the volumes of business and profits generated by both the finance and transport sectors was affected by the economic downturn (less people in work would mean less people getting peak time transport, less mortgages and loans were processed in the financial sector), a lot of their services are required regardless of economic factors (regular transportation, maintaining accounts and processing transactions), so while profits may have been down, the number of people employed could not decrease as quickly. Also, people in these industries can have more secure employment contracts so lay-offs might not happen as quickly.
Of these industries, some either appear to recover fully (transport) or partially (finance, construction). However, retail seems to be continuing an overall downward trajectory. While all sectors would have been affected by the downturn in the economy, there was a 5-fold increase in online shopping in Ireland between 2007 and 2017 [1], which would affect the recovery of high street retail. Also, since many of the large online retailers are not based in Ireland (e.g. ASOS and Amazon are UK based), the shift to online shopping would further reduce the numbers of people employed in retail in Dublin and Ireland.
Employment in IT have been increasing steadily since the end of 2010. This has been a growing industry in Ireland, and specifically Dublin due to the arrival of several international Tech companies such as AirBnb (2013) and Facebook (2008).
As the number of tourists coming to Ireland and Dublin has increased in recent years [2], you would expect that the number of people employed in the tourism sector has increased. Since the recession between 2007 and 2009 was a global recession, you would expect the number of tourists coming to Ireland in this period would have been impacted.
As employment in the scientific sector largely depends on government and industry sponsorship and grants which are often for a fixed period of time such as 3-5 years, we can see why the time period immediately after the recession had the lowest rates, if less new grants were awarded during the recession period, this would impact the number of people employed in the following years.
1.2 Dublin Property Trends
The timeperiod in this dataset is from Q3 07 to Q3 16. Over this time, the balance of rent vs buy has changed in Dublin. For the time period Q3 07 to Q3 11, both house prices and houses built outstripped both houses and apartments rented. For the time period Q2 13 to Q3 16, the situation is reversed and both house and apartment rental outstripped house prices and house buying.
Comparing this data to the construction employment figures in the question 1.1, we can see that the number of houses built and house prices behave in a similar pattern to the construction employment figures. The highest rates for house prices, houses built and construction jobs are at Q3 2007, Q3 2007 and Q4 2007 respectively. It should be noted that this is at the start of the dataset for house prices and houses being built so there may have been higher rates before this. The construction employment data started in Q1 2006 so we can say that Q4 2007 is the local maximum rate. Similarly, the lowest rates for house prices, houses built and construction jobs are Q3 2012, Q2 2012 and Q1 2013 respectively.
| Category | Year.min | Trend.min | Time.min | Year.max | Trend.max | Time.max | Trend.diff |
|---|---|---|---|---|---|---|---|
| Houses Built | Q2 12 | -0.78 | 20 | Q3 07 | 3.1 | 1 | 3.9 |
| House Prices | Q3 12 | -1.17 | 21 | Q3 07 | 2.2 | 1 | 3.4 |
| House Rental | Q1 11 | -1.25 | 15 | Q2 16 | 1.7 | 36 | 3.0 |
| Apt Rental | Q3 10 | -1.20 | 13 | Q3 16 | 2.0 | 37 | 3.2 |
| Construction | 1’13 | -1.09 | 29 | 4’07 | 1.8 | 8 | 2.9 |
Task 2: Real Time Bike Info
2.1 Summarise information available
As Dublin has quite a high number of stations and I’m most familiar with Dublin so I’ll choose that.
At the time that the data was obtained through the API: 2018-11-25 16:39:06, all 114, all Dublin bike stands were open. The bike stands are numbered 1 to 115, there is no bike stand number 20.
People generally access Dublin bikes through a subscription service. For 25 euro per year, you get a card that allows you unlimited bike rides of 30 minutes or less. It is possible to buy a 3 day pass with a credit card but this isn’t available at the majority of stations. There are payment facilities available at 37 of the 114 bike stations.
There are 12 columns in the dataset but several are redundant. As we used contract name to chose the data from Dublin, this column doesn’t give us any additional information. The name and address columns are identical in most cases. There are 3 cases where they differ but these are either due to spelling or punctuation differences, or a different description of where the station is located, name: GEORGES LANE, address: George’s Lane and name: SIR PATRICK DUN’S, address: Sir Patrick’s Dun. Bike stand number 5 is at an intersection and the name, CHARLEMONT PLACE and address: Charlemont Street are each road in the intersection. Therefore for our purposes, we will just use the address field as it’s more readable. The bonus field also doesn’t appear to add any information, it is false in every case. The latitude and longitude of each station is also provided. Aside from the addition of some unneeded fields, the dataset is complete, there are no NAs in fields of interest.
The number of bike stands per station vary between16 and 40, with an average of 32.05. At this point in time, there are 12 stations with no available bikes. There are 12 stations with no free bike stands, so someone would be unable to return a bike they had taken out. There is an average of 11.21 available bikes and 20.75 available bike stands per station. The number of available bikes and stands for each station can be seen in Figure 3 below. As the total number of bike stands is the sum of the available bike and available stands, the size of the station can also be seen in the figure below.
2.2 Plot Dublin Bikes Data
If someone quickly needed to get a Dublin bike, but didn’t have a Dublin bike card already set up, they would need a bike station with payment facilities in order to buy a 3 day pass. Below is a plot of the number of available bikes by station and whether the bike station takes payment.
Task 3 Dublin Bus
3.1 Shaping and describing the data
There are 119 unique routes in this dataset, all of which are route type 3, ‘Bus. Used for short- and long-distance bus routes.’ [1]. There was a total of 16846 different possible trips. The number of daily journeys for each route, split out by service type (weekdays, Saturdays and Sunday/Bank Holiday) are shown in Figure 5 below.
The mean frequencies across all routes for each type of service are as follows: Saturday: 30.48 mins, Sunday/Bank Holiday: 37.52 mins and Weekday: 26.25 mins. The routes with the highest and lowest frequencies routes for each type of service, along with their frequency in minutes are shown in the table below.
| Service | Classification | Freq | Route | Route.name |
|---|---|---|---|---|
| Saturday | Most Fequent | 5 mins | 102 | Bus Interchange - DART Station |
| Saturday | Least Fequent | 345 mins | 40B | River Meade Avenue - Cumberland Street |
| Sunday/Bank Holiday | Most Fequent | 5 mins | 40 | Liffey Valley Shop Cen - Seamus Ennis Rd |
| Sunday/Bank Holiday | Least Fequent | 415 mins | 41B | Cemetery - Irish Life Mall |
| Weekday | Most Fequent | 1 mins | 46A | Train Station - Phoenix Pk Gate |
| Weekday | Least Fequent | 850 mins | 16C | Stone Masons Way - St. Martin’s Chapel |
The units of distance travelled are not specified in the dataset. The distance on the 16 route from the George’s Street stop to the Redmond’s Hill stop is given in the dataset is 491.34. The distance between these two stops according to Google maps is 500m, so it appears that the unit of distance is meters. The mean and median route length are very similar at 19.68 km and 19.84 km respectively. The shortest route is Route 332, Donabate Station - Opposite the Burrow Estate, at 4 km. The longest route is Route 33X, Barnageeragh Road - Huguenot Cemetery, at 43.75 km. Both can be seen in Figure X above.
The entire route map is shown below in Figure 6, overlaid over a map of Dublin. The longest (green) and shortest (red) routes are also shown. 
Pick a route
I live in Harold’s Cross and work in College Green so am picking Route 49, Cloonmore Drive - Grand Canal Quay. According to the data, direction_id = 1 is in the direction of Pearse St, so that’s the direction I would get to work. Direction_id = 0 is in the direction of The Square, so that would be the direction I would get home from work. The stops in question are listed below.| route_short_name | route_long_name | direction_id | stop_headsign | stop_id | stop_name |
|---|---|---|---|---|---|
| 49 | Cloonmore Drive - Grand Canal Quay | 0 | The Square | 8220DB004521 | College Green |
| 49 | Cloonmore Drive - Grand Canal Quay | 0 | The Square | 8220DB001296 | Harold’s Cross Rd |
| 49 | Cloonmore Drive - Grand Canal Quay | 1 | Pearse St | 8220DB001339 | Harold’s Cross Rd |
| 49 | Cloonmore Drive - Grand Canal Quay | 1 | Pearse St | 8220DB005192 | Fleet Street |
The trip into work should take 26.58 mins and 4.31 km. The trip home from work should take 24.35 mins and 4 km. Neither of these times appear to take into consideration traffic so I would have to look at timings in practice.
If I needed to arrive for work at between 8.15 and 9am, and want to get a bus home from work between 5pm and 5.45 pm, I’ll have to get one of the services below. As you can see, tehre are several options for coming home from work, but only two going to work in the morning.
| Service | Journey | Direction | stop_name | arrival_time |
|---|---|---|---|---|
| Weekday | Commute to home | The Square | College Green | 17:06:41 |
| Weekday | Commute to home | The Square | Harold’s Cross Rd | 17:31:02 |
| Weekday | Commute to home | The Square | College Green | 17:21:41 |
| Weekday | Commute to home | The Square | Harold’s Cross Rd | 17:46:02 |
| Weekday | Commute to home | The Square | College Green | 17:36:41 |
| Weekday | Commute to home | The Square | Harold’s Cross Rd | 18:01:02 |
| Weekday | Commute to work | Pearse St | Harold’s Cross Rd | 08:29:13 |
| Weekday | Commute to work | Pearse St | Fleet Street | 08:55:48 |
| Service | stop_headsign | earliest.bus | latest.bus |
|---|---|---|---|
| Saturday | Pearse St | 06:45:00 | 23:58:51 |
| Saturday | The Square | 06:45:00 | 23:58:00 |
| Sunday/Bank Holiday | Pearse St | 09:30:00 | 23:58:00 |
| Sunday/Bank Holiday | The Square | 10:30:00 | 23:59:36 |
| Weekday | Pearse St | 06:15:00 | 23:59:34 |
| Weekday | The Square | 06:10:00 | 23:59:00 |
I like exploring Dublin on a Saturday but I don’t have a car and I don’t like walking between stops to change bus. Here I’ll look at everywhere that I can get to, using buses that interconnect with my chosen route 49. This analysis can easily be changed to look at other routes by changing the bus route chosen above.
Task 4
## Time Mon.In Mon.Out Tue.In Tue.Out Wed.In Wed.Out Thurs.In Thurs.Out
## 1 00:00 0 0 35 106 19 37 34 30
## 2 01:00 0 0 42 78 4 7 10 11
## 3 02:00 0 0 37 78 6 12 2 14
## 4 03:00 0 0 28 145 2 1 5 10
## 5 04:00 0 0 24 75 4 5 3 6
## 6 05:00 0 0 9 7 8 6 9 5
## 7 06:00 0 0 30 14 18 11 15 19
## 8 07:00 0 0 21 21 57 29 65 34
## 9 08:00 0 0 16 20 86 61 80 75
## 10 09:00 0 0 21 14 86 100 122 113
## 11 10:00 0 0 35 33 146 172 124 178
## 12 11:00 0 0 78 78 225 259 251 280
## 13 12:00 0 0 105 109 333 363 356 390
## 14 13:00 0 0 148 147 460 395 455 466
## 15 14:00 0 0 166 172 433 342 390 376
## 16 15:00 0 0 183 143 364 362 399 341
## 17 16:00 0 0 174 120 361 331 355 337
## 18 17:00 0 0 185 167 302 308 312 336
## 19 18:00 0 0 150 108 250 195 281 264
## 20 19:00 0 0 100 83 172 137 184 185
## 21 20:00 0 0 80 66 125 127 145 139
## 22 21:00 0 0 45 55 70 95 98 90
## 23 22:00 0 0 57 44 72 56 65 57
## 24 23:00 0 0 46 35 3 9 76 52
## 25 00:00 29 20 17 16 31 13 19 22
## 26 01:00 7 11 4 6 5 16 6 13
## 27 02:00 1 8 7 8 8 13 5 6
## 28 03:00 3 8 3 14 4 5 0 19
## 29 04:00 3 8 2 2 1 1 0 1
## 30 05:00 3 12 8 2 5 3 5 10
## 31 06:00 18 15 23 9 16 8 21 13
## 32 07:00 66 54 54 49 49 31 65 33
## 33 08:00 134 82 124 114 121 110 119 103
## 34 09:00 133 134 103 124 119 126 133 129
## 35 10:00 127 135 166 197 159 211 145 199
## 36 11:00 189 206 235 230 242 252 185 189
## 37 12:00 240 256 269 288 282 290 257 274
## 38 13:00 403 403 438 388 413 377 359 348
## 39 14:00 334 313 336 316 373 345 270 255
## 40 15:00 273 231 292 287 323 318 278 286
## 41 16:00 268 249 305 269 275 257 257 261
## 42 17:00 240 248 284 259 274 289 251 298
## 43 18:00 232 185 240 167 277 191 254 250
## 44 19:00 140 127 152 123 181 148 182 161
## 45 20:00 98 84 115 124 106 99 158 108
## 46 21:00 67 66 75 71 68 70 93 88
## 47 22:00 42 51 56 31 64 57 68 72
## 48 23:00 31 27 2 2 36 41 53 40
## 49 00:00 41 27 13 10 16 25 3 6
## 50 01:00 8 15 8 10 3 14 7 17
## 51 02:00 3 16 2 14 5 18 7 6
## 52 03:00 3 8 5 10 2 12 4 18
## 53 04:00 2 4 1 4 6 1 4 7
## 54 05:00 5 8 7 6 9 2 6 4
## 55 06:00 14 12 30 13 29 10 24 12
## 56 07:00 39 33 56 35 59 38 62 42
## 57 08:00 71 60 102 95 114 104 117 95
## 58 09:00 105 121 141 129 144 131 156 143
## 59 10:00 179 196 158 188 153 199 168 184
## 60 11:00 267 260 204 233 209 209 218 248
## 61 12:00 320 313 296 290 258 292 291 348
## 62 13:00 380 346 372 368 406 378 410 374
## 63 14:00 356 319 330 284 272 314 371 281
## 64 15:00 334 271 288 295 314 249 304 278
## 65 16:00 263 234 266 280 252 222 269 249
## 66 17:00 248 185 303 273 225 217 264 257
## 67 18:00 181 144 231 232 220 189 256 177
## 68 19:00 104 88 196 141 130 143 147 191
## 69 20:00 82 83 102 102 91 87 104 104
## 70 21:00 66 55 84 82 79 71 81 62
## 71 22:00 50 56 89 43 52 62 41 48
## 72 23:00 49 35 69 28 65 50 42 45
## 73 00:00 18 17 15 12 6 19 35 26
## 74 01:00 2 13 5 13 7 20 8 25
## 75 02:00 1 14 6 11 2 10 5 24
## 76 03:00 2 6 0 5 3 19 4 24
## 77 04:00 3 3 0 1 0 3 1 2
## 78 05:00 11 11 10 10 18 10 8 2
## 79 06:00 33 15 26 7 24 8 23 9
## 80 07:00 67 37 46 45 63 51 45 33
## 81 08:00 129 86 119 97 139 102 145 93
## 82 09:00 146 121 173 116 153 112 150 128
## 83 10:00 135 154 162 157 137 148 188 212
## 84 11:00 178 206 186 218 210 228 225 232
## 85 12:00 222 242 249 229 260 290 293 293
## 86 13:00 328 314 367 397 391 344 419 394
## 87 14:00 269 279 311 272 333 283 337 306
## 88 15:00 255 254 231 249 270 283 307 300
## 89 16:00 269 247 255 263 267 267 257 299
## 90 17:00 225 261 239 241 272 281 272 278
## 91 18:00 210 176 245 200 260 180 223 228
## 92 19:00 147 127 155 126 185 142 190 151
## 93 20:00 111 108 83 85 96 136 157 131
## 94 21:00 56 50 91 59 67 81 123 76
## 95 22:00 55 55 50 61 73 60 80 82
## 96 23:00 33 27 42 36 53 41 63 35
## 97 00:00 10 24 7 13 43 29 31 24
## 98 01:00 3 20 4 9 1 11 4 13
## 99 02:00 5 10 3 2 6 14 6 7
## 100 03:00 1 4 2 6 2 18 2 18
## 101 04:00 2 6 1 4 4 14 2 11
## 102 05:00 9 0 14 4 14 1 4 0
## 103 06:00 21 15 23 14 20 9 26 18
## 104 07:00 52 36 61 43 43 45 39 27
## 105 08:00 115 95 157 127 135 115 119 75
## 106 09:00 153 133 155 140 161 157 140 110
## 107 10:00 187 163 153 163 150 167 169 171
## 108 11:00 176 211 213 244 136 159 235 261
## 109 12:00 257 275 321 323 244 210 294 298
## 110 13:00 389 322 461 387 375 332 393 386
## 111 14:00 289 296 272 290 263 225 243 262
## 112 15:00 281 234 219 237 248 255 224 258
## 113 16:00 247 259 238 206 299 319 250 285
## 114 17:00 275 230 259 251 295 297 240 272
## 115 18:00 186 164 262 169 272 203 247 230
## 116 19:00 170 135 145 137 202 157 195 145
## 117 20:00 105 77 121 112 96 112 143 98
## 118 21:00 72 67 82 104 75 89 113 83
## 119 22:00 61 46 51 49 87 55 63 68
## 120 23:00 5 8 41 57 67 40 80 45
## 121 00:00 22 23 14 21 25 21 12 18
## 122 01:00 19 37 5 10 0 9 15 23
## 123 02:00 4 14 4 5 3 10 4 6
## 124 03:00 16 20 1 1 2 15 1 15
## 125 04:00 1 0 0 3 2 5 0 2
## 126 05:00 13 6 8 2 9 3 7 4
## 127 06:00 17 11 17 5 27 11 18 8
## 128 07:00 46 34 41 29 47 44 53 30
## 129 08:00 156 137 124 112 147 102 116 102
## 130 09:00 148 134 153 156 148 151 170 160
## 131 10:00 180 194 153 164 158 206 175 174
## 132 11:00 224 219 195 220 232 249 200 208
## 133 12:00 286 302 318 304 335 292 250 252
## 134 13:00 402 389 365 404 447 390 300 334
## 135 14:00 316 241 252 284 327 315 298 285
## 136 15:00 278 250 239 233 322 294 219 260
## 137 16:00 246 267 251 265 298 296 298 292
## 138 17:00 256 288 252 249 295 297 239 280
## 139 18:00 243 179 227 182 284 224 273 229
## 140 19:00 135 97 143 134 158 200 163 155
## 141 20:00 101 65 130 103 124 121 136 121
## 142 21:00 68 61 64 58 94 60 88 88
## 143 22:00 53 32 60 56 63 73 74 68
## 144 23:00 21 29 53 39 35 49 50 61
## 145 00:00 10 26 17 15 13 25 15 29
## 146 01:00 5 16 6 11 5 12 20 32
## 147 02:00 6 11 2 4 4 14 14 19
## 148 03:00 4 11 0 8 0 8 4 10
## 149 04:00 3 12 3 8 1 0 1 5
## 150 05:00 10 6 4 0 8 7 7 4
## 151 06:00 23 18 22 10 24 9 16 11
## 152 07:00 54 37 38 30 76 42 49 30
## 153 08:00 112 99 153 95 155 99 140 84
## 154 09:00 170 152 149 119 132 136 141 134
## 155 10:00 193 193 181 182 160 177 177 217
## 156 11:00 265 203 268 258 229 245 270 296
## 157 12:00 285 304 328 331 306 305 286 360
## 158 13:00 406 380 470 406 440 424 490 446
## 159 14:00 330 320 351 323 384 328 376 393
## 160 15:00 305 272 280 320 313 325 363 349
## 161 16:00 310 287 291 298 374 314 403 298
## 162 17:00 283 269 284 261 298 312 348 362
## 163 18:00 214 153 214 161 274 198 315 231
## 164 19:00 119 111 137 141 163 160 251 196
## 165 20:00 103 87 84 103 102 88 213 165
## 166 21:00 64 67 80 79 110 92 154 73
## 167 22:00 31 39 60 37 71 73 112 60
## 168 23:00 40 48 49 44 51 58 88 74
## 169 00:00 13 13 15 24 14 23 25 30
## 170 01:00 9 22 9 3 10 12 11 18
## 171 02:00 1 15 0 12 0 11 6 11
## 172 03:00 0 4 4 15 0 6 4 13
## 173 04:00 1 7 0 3 1 1 1 0
## 174 05:00 15 3 12 1 20 6 8 1
## 175 06:00 21 12 16 20 21 16 22 10
## 176 07:00 49 36 47 31 50 39 47 52
## 177 08:00 115 91 124 111 143 118 133 91
## 178 09:00 203 144 155 142 153 153 151 143
## 179 10:00 178 168 155 185 194 197 183 226
## 180 11:00 222 242 232 240 268 245 249 255
## 181 12:00 335 340 307 327 297 317 323 342
## 182 13:00 467 479 439 409 468 478 464 365
## 183 14:00 334 355 347 321 357 293 340 361
## 184 15:00 352 287 302 298 301 280 294 285
## 185 16:00 323 291 326 301 284 313 275 303
## 186 17:00 307 315 298 334 257 297 301 329
## 187 18:00 286 233 276 228 234 202 230 193
## 188 19:00 157 180 167 153 191 144 188 142
## 189 20:00 97 98 121 100 99 145 153 124
## 190 21:00 70 61 83 85 82 106 79 89
## 191 22:00 47 39 46 49 82 62 75 72
## 192 23:00 47 32 50 48 57 63 70 57
## 193 00:00 21 34 6 24 16 34 23 29
## 194 01:00 3 13 1 10 5 19 4 17
## 195 02:00 4 8 1 2 5 10 3 12
## 196 03:00 2 11 3 5 7 12 3 13
## 197 04:00 1 0 0 0 0 0 2 8
## 198 05:00 7 5 6 1 12 1 11 3
## 199 06:00 15 7 19 9 16 12 16 11
## 200 07:00 52 40 48 33 52 37 40 39
## 201 08:00 120 107 131 123 130 118 115 119
## 202 09:00 176 135 167 127 164 134 173 132
## 203 10:00 171 184 139 205 191 200 175 225
## 204 11:00 236 232 244 258 206 241 245 235
## 205 12:00 320 335 343 315 319 337 310 282
## 206 13:00 448 391 404 432 423 441 460 397
## 207 14:00 340 271 342 321 330 365 372 355
## 208 15:00 308 277 312 284 292 314 304 304
## 209 16:00 287 293 283 278 288 292 335 312
## 210 17:00 318 299 250 219 316 269 314 329
## 211 18:00 281 207 245 221 283 190 286 254
## 212 19:00 158 129 142 134 192 164 190 180
## 213 20:00 110 123 113 111 130 133 155 155
## 214 21:00 78 54 81 89 91 87 99 74
## 215 22:00 40 51 54 50 46 83 90 91
## 216 23:00 27 23 54 34 54 45 52 42
## 217 00:00 27 34 6 25 11 24 35 33
## 218 01:00 9 12 6 6 7 17 6 16
## 219 02:00 1 20 1 5 0 15 13 19
## 220 03:00 3 6 0 10 0 6 5 12
## 221 04:00 0 3 0 0 0 1 0 4
## 222 05:00 10 2 10 3 14 6 7 5
## 223 06:00 17 5 20 6 11 10 16 13
## 224 07:00 53 34 58 37 58 37 46 25
## 225 08:00 139 122 122 118 178 123 168 121
## 226 09:00 161 141 185 140 150 155 169 186
## 227 10:00 159 201 183 205 209 219 171 197
## 228 11:00 261 247 262 283 236 270 278 268
## 229 12:00 271 328 332 315 288 320 280 332
## 230 13:00 448 421 517 508 377 387 394 414
## 231 14:00 369 318 351 353 381 331 306 261
## 232 15:00 309 296 315 304 295 308 275 294
## 233 16:00 301 264 305 308 325 278 275 296
## 234 17:00 290 279 298 290 282 304 286 312
## 235 18:00 266 199 253 201 256 181 243 169
## 236 19:00 167 143 161 159 144 143 164 127
## 237 20:00 111 83 117 109 117 105 161 207
## 238 21:00 70 83 87 97 94 84 98 92
## 239 22:00 65 39 72 69 61 71 63 67
## 240 23:00 30 63 35 31 39 28 67 66
## 241 00:00 22 32 26 26 29 21 14 26
## 242 01:00 4 11 2 7 5 9 15 17
## 243 02:00 4 8 4 7 3 4 7 9
## 244 03:00 23 34 0 11 0 18 4 10
## 245 04:00 40 34 1 3 0 10 2 4
## 246 05:00 11 0 6 1 9 5 9 5
## 247 06:00 17 7 16 6 18 10 17 11
## 248 07:00 38 36 30 34 47 30 46 36
## 249 08:00 118 94 118 94 110 98 117 104
## 250 09:00 150 152 144 143 155 165 161 151
## 251 10:00 139 135 188 194 202 211 215 226
## 252 11:00 180 221 212 224 244 269 284 279
## 253 12:00 262 237 342 309 324 298 357 359
## 254 13:00 385 365 420 473 476 429 510 421
## 255 14:00 325 268 343 341 331 323 356 336
## 256 15:00 302 230 329 292 324 329 335 299
## 257 16:00 288 254 288 291 314 252 291 315
## 258 17:00 249 231 275 234 276 228 347 338
## 259 18:00 222 181 262 217 245 183 315 272
## 260 19:00 137 132 176 189 189 154 248 192
## 261 20:00 118 92 139 106 98 123 204 182
## 262 21:00 66 87 98 76 73 42 148 112
## 263 22:00 54 56 71 47 64 64 100 99
## 264 23:00 48 46 40 45 22 59 59 59
## 265 00:00 63 129 7 34 19 24 28 11
## 266 01:00 54 103 6 13 16 10 9 5
## 267 02:00 29 104 1 4 3 9 0 8
## 268 03:00 25 92 0 1 4 5 0 11
## 269 04:00 23 24 2 4 4 7 3 10
## 270 05:00 8 17 7 5 5 3 7 5
## 271 06:00 13 11 19 8 15 6 19 12
## 272 07:00 25 17 47 37 50 31 47 43
## 273 08:00 32 21 111 112 130 110 124 116
## 274 09:00 47 27 148 124 157 138 167 145
## 275 10:00 88 125 205 193 208 216 236 221
## 276 11:00 203 137 214 227 245 241 258 288
## 277 12:00 260 231 280 212 342 317 350 339
## 278 13:00 291 233 333 330 498 421 309 324
## 279 14:00 314 276 269 248 362 363 233 246
## 280 15:00 338 261 227 218 331 287 224 224
## 281 16:00 286 250 252 252 105 113 213 228
## 282 17:00 196 167 244 244 332 269 261 228
## 283 18:00 195 119 239 206 267 204 213 195
## 284 19:00 107 89 166 111 174 166 136 140
## 285 20:00 104 89 85 108 114 117 113 85
## 286 21:00 48 49 92 66 90 61 84 64
## 287 22:00 38 35 42 36 84 43 48 49
## 288 23:00 33 32 35 35 49 45 37 33
## 289 00:00 15 24 8 24 14 29 51 38
## 290 01:00 9 21 6 10 11 16 11 19
## 291 02:00 1 16 2 11 8 4 4 12
## 292 03:00 1 9 0 12 0 3 6 11
## 293 04:00 3 6 5 11 2 4 5 5
## 294 05:00 9 1 15 11 11 5 8 7
## 295 06:00 13 13 12 7 32 31 14 11
## 296 07:00 33 31 43 26 72 65 39 34
## 297 08:00 119 86 123 64 162 110 111 69
## 298 09:00 138 123 130 121 152 127 145 127
## 299 10:00 192 175 157 154 194 187 180 189
## 300 11:00 248 263 214 252 207 239 287 244
## 301 12:00 356 329 338 311 247 245 354 344
## 302 13:00 476 367 408 351 433 364 475 432
## 303 14:00 353 347 389 300 311 304 437 397
## 304 15:00 337 292 283 294 356 281 382 395
## 305 16:00 323 272 281 292 349 272 394 362
## 306 17:00 292 289 239 251 493 397 361 381
## 307 18:00 248 188 293 198 265 205 298 270
## 308 19:00 146 134 178 173 212 166 233 216
## 309 20:00 102 105 103 72 126 95 160 190
## 310 21:00 83 65 63 70 104 99 135 119
## 311 22:00 41 63 57 75 73 74 108 85
## 312 23:00 28 44 39 50 62 46 67 80
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## 1202 749 1615 2443 2114 8772 Week_51
## 1203 637 1329 2220 1841 7578 Week_51
## 1204 782 1560 2261 2151 8304 Week_51
## 1205 402 805 1456 1200 4876 Week_51
## 1206 269 351 581 544 2380 Week_51
## 1207 635 628 558 252 4075 Week_51
## 1208 1701 1595 665 330 9440 Week_51
## 1209 3590 3529 1322 627 20218 Week_51
## 1210 3371 3998 3083 1671 21636 Week_51
## 1211 4397 5095 5625 3756 31418 Week_51
## 1212 6417 7261 7980 6209 46078 Week_51
## 1213 8769 8508 8540 8312 58379 Week_51
## 1214 11737 10806 9949 10155 72901 Week_51
## 1215 9202 9666 9573 10205 62964 Week_51
## 1216 8524 9825 10806 10581 64096 Week_51
## 1217 9789 9816 8812 9934 60117 Week_51
## 1218 9317 9129 8147 8563 60713 Week_51
## 1219 10059 7264 7389 3687 52272 Week_51
## 1220 8230 6306 5313 2478 39030 Week_51
## 1221 6160 4910 3666 1673 27315 Week_51
## 1222 3624 3110 3225 1117 17595 Week_51
## 1223 2436 2343 2643 781 12727 Week_51
## 1224 2291 2857 2483 831 12802 Week_51
## 1225 75 540 887 1591 4938 Week_52
## 1226 11 371 702 1274 3545 Week_52
## 1227 23 580 603 1058 3495 Week_52
## 1228 16 1056 638 1402 4167 Week_52
## 1229 13 587 362 882 2440 Week_52
## 1230 107 213 174 344 1348 Week_52
## 1231 234 311 358 175 1607 Week_52
## 1232 471 625 601 285 3062 Week_52
## 1233 1320 980 832 416 6188 Week_52
## 1234 3392 1598 1615 823 11620 Week_52
## 1235 5613 2715 3301 2062 20271 Week_52
## 1236 6448 4740 5693 3960 33092 Week_52
## 1237 6776 5553 7350 5690 42606 Week_52
## 1238 7766 6609 8745 6709 47328 Week_52
## 1239 7585 7089 8636 7277 48969 Week_52
## 1240 7264 6640 9284 7683 48185 Week_52
## 1241 7019 7396 9415 7763 47060 Week_52
## 1242 4907 6090 7381 6550 36784 Week_52
## 1243 2664 4172 5913 4809 26116 Week_52
## 1244 1410 2773 3857 2782 16597 Week_52
## 1245 621 1898 2554 1941 10451 Week_52
## 1246 426 1406 1862 1305 6912 Week_52
## 1247 479 1218 1766 1036 5876 Week_52
## 1248 887 1126 1935 1052 6089 Week_52The footfall in Capel Street (average per week over 2013 is 5.1710^{4}) tends to be much lower than that in Grafton street (average per week over 2013 is 5.4410^{5}), which would be expected as Grafton is a very busy shopping street and Capel Street would be expected to have less traffic. We can see the amount of footfall over the course of the year in Figure X below.
Task 2.2
I chose an ambient sound dataset
## # A tibble: 1,187 x 11
## Week Time mon.noise tue.noise wed.noise thurs.noise fri.noise
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Week~ 00:00 0 61.8 54.1 56.7 56.0
## 2 Week~ 01:00 0 59.2 52.0 52.4 52.3
## 3 Week~ 02:00 0 59.6 49.6 50.7 50.8
## 4 Week~ 03:00 0 58.0 51.5 51.2 52.1
## 5 Week~ 04:00 0 59.3 54.4 53.5 53.3
## 6 Week~ 05:00 0 57.0 56.6 57.6 55.9
## 7 Week~ 06:00 0 56.7 58.5 59.0 58.5
## 8 Week~ 07:00 0 57.8 61.1 60.7 60.7
## 9 Week~ 08:00 0 57.7 62.1 61.7 62.0
## 10 Week~ 09:00 0 57.4 61.9 61.5 61.5
## # ... with 1,177 more rows, and 4 more variables: sat.noise <dbl>,
## # sun.noise <dbl>, Week.fact <fct>, All <dbl>## # A tibble: 50 x 2
## Week.fact Sound_Level
## <fct> <dbl>
## 1 Week_01 8415.
## 2 Week_02 9938.
## 3 Week_03 10053.
## 4 Week_04 10056.
## 5 Week_05 10047.
## 6 Week_06 10070.
## 7 Week_07 10004.
## 8 Week_08 631.
## 9 Week_11 5918.
## 10 Week_12 10449.
## # ... with 40 more rows## # A tibble: 52 x 3
## Week.fact Footfall Camera
## <fct> <dbl> <chr>
## 1 Week_01 38307 Capel St at Mullen
## 2 Week_02 45547 Capel St at Mullen
## 3 Week_03 42236 Capel St at Mullen
## 4 Week_04 42849 Capel St at Mullen
## 5 Week_05 44797 Capel St at Mullen
## 6 Week_06 45421 Capel St at Mullen
## 7 Week_07 50637 Capel St at Mullen
## 8 Week_08 48467 Capel St at Mullen
## 9 Week_09 49715 Capel St at Mullen
## 10 Week_10 49294 Capel St at Mullen
## # ... with 42 more rows
## Time Mon Label
## 1 00:00 192 Footfall
## 2 01:00 157 Footfall
## 3 02:00 133 Footfall
## 4 03:00 117 Footfall
## 5 04:00 47 Footfall
## 6 05:00 25 Footfall
## 7 06:00 24 Footfall
## 8 07:00 42 Footfall
## 9 08:00 53 Footfall
## 10 09:00 74 Footfall
## 11 10:00 213 Footfall
## 12 11:00 340 Footfall
## 13 12:00 491 Footfall
## 14 13:00 524 Footfall
## 15 14:00 590 Footfall
## 16 15:00 599 Footfall
## 17 16:00 536 Footfall
## 18 17:00 363 Footfall
## 19 18:00 314 Footfall
## 20 19:00 196 Footfall
## 21 20:00 193 Footfall
## 22 21:00 97 Footfall
## 23 22:00 73 Footfall
## 24 23:00 65 Footfall## Time Mon Label
## 1 00:00 192 Footfall
## 2 01:00 157 Footfall
## 3 02:00 133 Footfall
## 4 03:00 117 Footfall
## 5 04:00 47 Footfall
## 6 05:00 25 Footfall
## 7 06:00 24 Footfall
## 8 07:00 42 Footfall
## 9 08:00 53 Footfall
## 10 09:00 74 Footfall
## 11 10:00 213 Footfall
## 12 11:00 340 Footfall
## 13 12:00 491 Footfall
## 14 13:00 524 Footfall
## 15 14:00 590 Footfall
## 16 15:00 599 Footfall
## 17 16:00 536 Footfall
## 18 17:00 363 Footfall
## 19 18:00 314 Footfall
## 20 19:00 196 Footfall
## 21 20:00 193 Footfall
## 22 21:00 97 Footfall
## 23 22:00 73 Footfall
## 24 23:00 65 Footfall## # A tibble: 24 x 3
## Time Mon Label
## <chr> <dbl> <chr>
## 1 00:00 59.5 Ambient Noise
## 2 01:00 58.8 Ambient Noise
## 3 02:00 58.1 Ambient Noise
## 4 03:00 59.0 Ambient Noise
## 5 04:00 59.3 Ambient Noise
## 6 05:00 59.2 Ambient Noise
## 7 06:00 61.5 Ambient Noise
## 8 07:00 62.5 Ambient Noise
## 9 08:00 61.3 Ambient Noise
## 10 09:00 61.6 Ambient Noise
## # ... with 14 more rows
5 Reflection
5.1 Analytic
Here I analyse the the history of my log console for the duration of this assignment.
I ran a total of 8649 lines of code, which contained 63991 words. I ran on average, 201.14 commands an hour, or 3.35 per minute, with a median of 2.87 commands per minute and a maximum of 13.93 per minute. As I was running a lot of for loops, this high rate isn’t unexpected.
The most frequently run commands involved, as expected, assignment and piping:, <- at 4246 times, %>% at 3658 times and = at 2562 times. The most common R functions were distinct() at 317 and nrow at 268, which I found a bit surprising until I realised that I use nrow in the footfall loop to read in the data and I used distinct() in the bus route data, which I had a bit of trouble with. The most common variables were the column names I gave to the footfall columns such as mon.in, mon.out etc, at231 each. As these run inside a loop to read in all the footfall data, it’s not surprising that these were the most common.
As I’ve been trying to get better at using the built in R help, I measured the number of times I used the help function by looking at the number of times ‘?’ was used. I probably still have to get better at using it, as you can see from the figure below.
I can see from the graph that I tended to work in large chunks, not taking many breaks. This isn’t ideal for concentration, but as I have other commitments, it’s the only way I can fit in the work.
5.2 Personal Reflection
1.) challenges overcome (work-arounds), 2.) efficient work practices 3.) areas of frustration 4.) time management. A useful way to structure a reflection is by using the four I’s e.g. Identify an element during the project (past) - results, quality, emotions, productivity. Investigate - determine the source of this (present). Incorporate - based on the investigation, is there a lesson to absorb (future)? Improve - the goal of reflection.
The length of the project was the main challenge for me. Some of the parts were quite complex but I enjoyed figuring them out, my main issue was finding time to do everything.
I decided to approach the project by attempting each problem until I got stuck and then moving on to the next one if I was stuck for more than 20 minutes or so, and then going back to the start to finish the problems to the required standard. I’ve learned from previous experience that it’s easier for me to work on improving a question rather than starting something from scratch. By looking at each question in some detail at the start, is also allows ideas to peculate, so when you come back to it later, you might have solved the problem or have a new perspective. When I actually stuck to this, it worked well, but a few times I kept at something when I just should have given up and moved on, at least for the moment. Towards the end of the assignment, I made a Trello to do list which had the ‘minimum viable answer’ for each task, so I would stick to doing that. I ca go back and improve it later.
I found some of the tasks challenging, like finding a dataset to compare to the footfall one, mainly because the footfall data was from 2013 and it was difficult to find an easily accessible dataset from that time.
It’s also easy to get stuck down a rabbit hole of trying to solve a particular technical problem but then when you look at it with a fresh mind, you realised that problem didn’t need to be solved at all for the question.
Got stuck down a rabbit hole of joining together dataframes in task 3 and got stuck. Came back to it another day and reread the question and realised I was making it overly complicated.
I sometimes found it hard to figure out whether there was a small issue with my code and I was almost there, or if I was completely wrong: loading bike JSON.
As some of the questions were of the type ‘find the most interesting information’, I decided to first so something relatively basic and straightforward and then come back to it at a later date and improve it if I had time. This would also hopefully give me time to come up with a better idea.
Because we have a test coming up where we won’t have internet access, I’ve started using the built in R help function instead of googling the same information, which I think it a better habit to get into as you get to understand the inner workings of the function instead of letting someone else interpret it for you.
[1] https://www.irishexaminer.com/ireland/fivefold-increase-in-online-shopping-since-2007-469059.html (original Euromonitor International report not freely available online)
[2] https://www.cso.ie/px/pxeirestat/Database/eirestat/Overseas%20Travel/Overseas%20Travel_statbank.asp?SP=Overseas%20Travel&Planguage=0y (report TMA14: Overseas Trips to and from Ireland by Trips and Year)